World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
16672
World Ranking
4215
National Ranking
563

Overview

Lequan Yu is affiliated with the University of Hong Kong in China and has contributed extensively to research in computer science and medicine, with a primary focus on medical imaging and artificial intelligence applications.

Their work covers a range of topics including:

  • Radiomics and Machine Learning in Medical Imaging
  • Advanced Neural Network Applications
  • AI in Cancer Detection
  • Domain Adaptation and Few-Shot Learning
  • Medical Image Segmentation Techniques
  • COVID-19 Diagnosis Using AI
  • Medical Imaging Techniques and Applications

Lequan Yu has a substantial publication record in prominent venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • Medical Image Analysis
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Journal of Biomedical and Health Informatics

Among their recent papers are:

  • Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation, 2020, IEEE Transactions on Neural Networks and Learning Systems
  • nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer, 2023, IEEE Transactions on Image Processing
  • nnFormer: Interleaved Transformer for Volumetric Segmentation, 2021, arXiv (Cornell University)
  • Uncertainty-aware Multi-view Co-training for Semi-supervised Medical Image Segmentation and Domain Adaptation, 2020, Medical Image Analysis
  • DoFE: Domain-Oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets, 2020, IEEE Transactions on Medical Imaging

Frequent collaborators in their research include:

  • Lei Xing (32 coauthored works)
  • Pheng-Ann Heng (25 coauthored works)
  • Shujun Wang (19 coauthored works)
  • Liansheng Wang (17 coauthored works)
  • Weiqin Zhao (15 coauthored works)

Their research spans significant subfields such as:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Biomedical Engineering
  • Pulmonary and Respiratory Medicine

Lequan Yu's interdisciplinary work bridges technical and medical domains, reflecting a focus on the development and application of machine learning techniques for medical image analysis and diagnostic processes.

Best Publications

  • Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

    Lequan Yu;Hao Chen;Qi Dou;Jing Qin

  • Uncertainty-Aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

    Lequan Yu;Shujun Wang;Xiaomeng Li;Chi Wing Fu

  • VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images

    Hao Chen;Qi Dou;Lequan Yu;Jing Qin

  • Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

    Qi Dou;Hao Chen;Lequan Yu;Lei Zhao

  • DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

    Hao Chen;Xiaojuan Qi;Lequan Yu;Pheng-Ann Heng

  • PU-Net: Point Cloud Upsampling Network

    Lequan Yu;Xianzhi Li;Chi-Wing Fu;Daniel Cohen-Or

  • 3D deeply supervised network for automated segmentation of volumetric medical images.

    Qi Dou;Lequan Yu;Hao Chen;Yueming Jin

  • Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection

    Qi Dou;Hao Chen;Lequan Yu;Jing Qin

  • nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer

    Unknown

  • DCAN: Deep contour-aware networks for object instance segmentation from histology images

    Hao Chen;Xiaojuan Qi;Lequan Yu;Qi Dou

  • Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation

    Xiaomeng Li;Lequan Yu;Hao Chen;Chi-Wing Fu

  • MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data

    Quande Liu;Qi Dou;Lequan Yu;Pheng Ann Heng

  • Semi-Supervised Medical Image Classification With Relation-Driven Self-Ensembling Model

    Quande Liu;Lequan Yu;Luyang Luo;Qi Dou

  • Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge

    Jorge Bernal;Nima Tajkbaksh;Francisco Javier Sanchez;Bogdan J. Matuszewski

  • CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading

    Xiaomeng Li;Xiaowei Hu;Lequan Yu;Lei Zhu

  • Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d MR images

    Lequan Yu;Xin Yang;Hao Chen;Jing Qin

  • 3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

    Qi Dou;Hao Chen;Yueming Jin;Lequan Yu

  • SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network

    Yueming Jin;Qi Dou;Hao Chen;Lequan Yu

  • Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation

    Shujun Wang;Lequan Yu;Xin Yang;Chi-Wing Fu

  • EC-Net: An Edge-Aware Point Set Consolidation Network

    Lequan Yu;Xianzhi Li;Chi-Wing Fu;Daniel Cohen-Or

  • Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos

    Lequan Yu;Hao Chen;Qi Dou;Jing Qin

  • Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation

    Xiaomeng Li;Lequan Yu;Hao Chen;Chi-Wing Fu

Frequent Co-Authors

Pheng-Ann Heng
Pheng-Ann Heng Chinese University of Hong Kong
Chi-Wing Fu
Chi-Wing Fu Chinese University of Hong Kong
Qi Dou
Qi Dou Chinese University of Hong Kong
Jing Qin
Jing Qin Hong Kong Polytechnic University
Hao Chen
Hao Chen Chinese University of Hong Kong
Lei Xing
Lei Xing Stanford University
Dong Ni
Dong Ni Shenzhen University
Daniel Cohen-Or
Daniel Cohen-Or Tel Aviv University
Alan L. Yuille
Alan L. Yuille Johns Hopkins University
Vincent Mok
Vincent Mok Chinese University of Hong Kong

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Pursuing Computer Science in the USA opens the door to a diverse range of related degrees and technical career pathways, especially through online study. Fields like environmental engineering and mechanical engineering share strong foundations in science, problem-solving, and technology. For those seeking affordable options, consider an online environmental engineering degree science and engineering program or look for the cheapest mechanical engineering degree online. These pathways are ideal for students who wish to combine their programming skills with engineering expertise.

If your interests lean toward abstraction and fundamental sciences, an online bachelor's degree in physics offers a rigorous approach to learning, often overlapping with computational techniques taught in Computer Science. Additionally, data science is a booming field requiring core computational skills. You can follow a data science learning path for lucrative roles in analytics and AI.

With flexible schedules and a variety of specializations, online degrees provide excellent alternatives for building strong technical foundations and expanding your career prospects beyond traditional Computer Science.

Best Scientists Citing Lequan Yu

Trending Scientists

Recently Published Articles